CS 160: Lecture 25

1. CS 160: Lecture 25 Professor John Canny Fall 2004

2. Outline • Review of learning principles • Constructivism, Transfer, ZPD, Meta-cognition • Systems: • Constructivist learning • Collaborative learning • Meta-cognition • Inquiry-based environments • Tutors

3. Learning and existing knowledge • Learning is a process of building new knowledge using existing knowledge. • Knowledge is not acquired butconstructed out of existing“materials”. • The process of applying existingknowledge in new settings is called Transfer.

4. ZPD • Learning is layered and incremental. • In real societies, learners are helped by others. • In fact learners have a “zone” of concepts they can acquire with help. • This is the Zone of Proximal Development (ZPD).

5. Learning and experience • Learning is most effective when it connects with the learner’s real-world experiences. • The knowledge that the learner already has form those experiences serves as a foundation for knew knowledge.

6. Transfer and understanding • Transfer depends on thorough learning in the first situation (learning with understanding*). • The more thorough the understanding in the first situation, the more easily knowledge will transfer.

7. Transfer and Generality • Generality of existing knowledge: has the learner already seen it applied in several contexts?

8. Transfer and Motivation • Motivation: is the new knowledge useful or valuable? • Motivation encourages the user to visualize use of the new knowledge, and to try it out in new situations. • Students are usually motivated when the knowledge can be applied to everyday situations.

9. Transfer and Abstraction • Is the existing knowledge abstract or specific? • Abstract knowledge is packaged for portability. Its built with virtual objects and rules that can model many real situations. • E.g. clipart

10. Metacognition • Metacognition is the learner’s conscious awareness of their learning process. Metacognitionhelps transfer

11. Metacognition • Strong learners carefully manage their learning. • For instance, strong learners reading a textbook will pause regularly, check understanding, and go back to difficult passages. • Weak learners tend toplough through theentire text, then realize they don’tunderstand and startagain. Have I learnedthis yet?

12. Learning systems • Constructivist Systems • Logo, Microworlds, Boxer • Group-learning Systems • CoVis, TVI, Livenotes • Meta-Cognitive Systems • SMART, CSILE/Knowledge Forum • Inquiry-Based Systems • ThinkerTools • Automatic Tutors • Inquiry Island

13. Logo • The Logo project began in 1967 at MIT. • Seymour Papert had studied with Piaget in Geneva. He arrived at MIT in the mid-60s. • Logo often involved control of a physical robot called a turtle. • The turtle was equipped with apen that turned it into a simpleplotter – ideal for drawing math.shapes or seeing the trace of asimulation.

14. Logo • Early deployments of Logo in the 1970s happened in NYC and Dallas. • In 1980, Papert published “Mindstorms” outlining a constructivist curriculum that leveraged Logo. • Logo for Lego began in the mid-1980s under Mitch Resnick at MIT.

15. Logo • The “Microworlds” programming environment was created by Logo’s founders in 1993. It made better use of GUI features in Macs and PCs than Logo. • In 1998, Lego introducedMindstorms which had a Logo programming language with a visual “brick-based” interface.

16. Logo • Logo was widely deployed in schools in the 1990s. • Logo is primarily a programming environment, and assignments need to be programmed in Logo. • Unfortunately, curricula were not always carefully planned, nor were teachers well-prepared to use the new technology. • This led to a reaction against Logo from some educators in the US. It remains very strong overseas (e.g. England, South America).

17. Uses of Logo • Logo is designed to create “Microworlds” that students can explore. • The Microworld allows exploration and is “safe,” like a sandbox. • Children “discover” new principles by exploring a Microworld. • e.g. they may repeat some physics experiments to learn one of Newton’s laws.

18. Boxer • Boxer is a new system developed at Berkeley by Andy diSessa (one of the creators of Logo). • Boxer uses geometry (nested boxes) to represent nested procedure calls. • It has a faster learning curve in most cases than pure Logo.

19. Strengths of Logo • Very versatile. • Can create animations and simulations quickly. • Avoids irrelevant detail. • Tries to create “experiences” for students (from simulations). • Provides immediate feedback – students can change parameters and see the results right away. • Representations are rather abstract – which helps knowledge transfer.

20. Weaknesses of Logo • Someone else has to program the simulations etc – their design may make the “principle” hard to discover. Usability becomes an issue. • The “experience” with Logo/Mindstorms is not real-world, which can weaken motivation and learning. • The “discovery” model de-emphasizes the role of peers and teachers. • It does not address meta-cognition.

21. Break

22. Collaborative Software • CoVis (Northwestern, SRI) was a system for collaborative visualization of data for science learning, primarily in geo-science, 1994-… • Students work online with each other, and with remote experts. • They take virtual fieldtrips, or work with shared simulations.

23. CoVis • CoVis included a “Mentor database” of volunteer experts that teachers could tap to talk about advanced topics. • It also included a collaboratory notebook. The notebook included typed links to guide the student through their inquiry process. • Video-conferencing and screen-sharing were used to facilitate remote collaboration.

24. TVI • TVI (Tutored Video Instruction) was invented by James Gibbons, a Stanford EE Prof, in 1972. • Students view a recorded lecture in small groups (5-7) with a Tutor. They can pause, replay, and talk over the video. • The method works witha live student group, butalso with a distributedgroup, as per the figureat right.

25. DTVI • Sun Microsystems conducted a large study of distributed TVI in 1999. • More than 1100 students participated. • The study showed significant improvementsin learning for TVIstudents, compared tostudents in the livelecture (about 0.3 sdev).

26. DTVI • The DTVI study produced a wealth of interesting results: • Active participation was high (more than 50% of students participated in > 50% of discussions). • Amount of discussion in the group correlated with outcomes (exam scores). • Salience of discussion did not significantly correlate with outcome (any conversation is helpful??).

27. Livenotes • TVI requires a small-group environment (small tutoring rooms). • Livenotes attempts to recreate the small-group experience in a large lecture classroom. • Students work in small virtual groups, sharing a common workspace with wireless Tablet-PCs. • The workspace overlaysPowerPoint lecture slides,so that note-taking andconversation are integrated.

28. Livenotes Interface

29. Livenotes Findings • The dialog between students happens spontaneously in graduate courses – where student discussion is common anyway. • It was much less common in undergraduate courses. • Students have different models of the lecture – something to be “captured” vs. some that is collaboratively created.

30. Livenotes Findings • But what was very common in undergraduate transcripts was student “dialog” with the PowerPoint slides: • Students oftenadd their ownbullets.

31. Livenotes Findings • Reinforcing/rejecting a bullet:

32. Livenotes Findings • Answering a question in a bullet:

33. Collaborative Systems • Given what you know about learning, list some advantages and disadvantages of the 3 systems (CoVis, TVI/DTVI, Livenotes).

34. Meta-Cognitive Systems • The SMART project (Vanderbilt, 1994-) gave students science activities with meta-cognitive scaffolds. • Students choose appropriate instruments to test their hypothesis – requiring them to understand the kind of information an instrument can give. • The case study was an environmental science course called the “Stones River Mystery”.

35. Meta-Cognitive Systems • The SMART lab required students to justify their choices – it encouraged them to reflect after their decisions, and hopefully while they are making them. • It also included several tools for collaboration between students. Explaining, asking questions, and reaching joint conclusions help improve meta-cognition.

36. Inquiry-Based Systems • A development of Piaget based on similarities between child learning and the scientific method. • In this approach, learners construct explicit theories of how things behave, and then test them through experiment. • The “ThinkerTools” system (White 1993) realized this approach for “force and motion” studies.

37. ThinkerTools • ThinkerTools uses an explicit inquiry cycle, shown below. • Students are scaffolded through the cycle by carefully-designed exercises.

38. ThinkerTools • ThinkerTools uses “reflective assessment” to help studentsgauge their own performanceand identify weaknesses.

39. ThinkerTools • The tools include simulation (for doing experiments) and analysis, for interpreting the results.

40. ThinkerTools • Students can modify the “laws of motion” in the system to see the results (e.g. F=a/m instead of ma).

41. Agents: Inquiry Island • An evolution of the ThinkerTools project. • Inquiry Island includes anotebook, which structuresstudents inquiry, and personified (software agent) advisers.

42. Inquiry Island • Task advisers: • Hypothesizer, investigator • General purpose advisers: • Inventor, collaborator, planner • System development advisers: • Modifier, Improver • Inquiry Island allows studentsto extend the inquiry scaffoldusing the last set of agents.

43. Question • How portable (across different courses) are these systems (SMART, ThinkerTools, Inquiry Island)?

44. Summary • We reviewed some learning principles from lec 5. • We gave some systems that roughly track the frontier of learning technology: • Constructivist systems • Collaborative systems • Meta-cognitive scaffolding systems • Inquiry systems • Automatic tutoring systems